Remote Sens. 2012, 4(3), 598-621; doi:10.3390/rs4030598
Using MODIS-NDVI for the Modeling of Post-Wildfire Vegetation Response as a Function of Environmental Conditions and Pre-Fire Restoration Treatments
1
School of Natural Resources and the Environment & Arizona Remote Sensing Center, 1955 E. Sixth Street, The University of Arizona, Tucson, AZ 85721, USA
2
School of Geography and Development, The University of Arizona, Tucson, AZ 85721, USA
3
Department of Biology, Robinson Science Hall 205, Whitworth University, 300 West Hawthorne Rd., Spokane, WA 99251, USA
*
Author to whom correspondence should be addressed.
Received: 31 January 2012 / Revised: 29 February 2012 / Accepted: 29 February 2012 / Published: 2 March 2012
(This article belongs to the Special Issue Advances in Remote Sensing of Wildland Fires)
Abstract
Post-fire vegetation response is influenced by the interaction of natural and anthropogenic factors such as topography, climate, vegetation type and restoration practices. Previous research has analyzed the relationship of some of these factors to vegetation response, but few have taken into account the effects of pre-fire restoration practices. We selected three wildfires that occurred in Bandelier National Monument (New Mexico, USA) between 1999 and 2007 and three adjacent unburned control areas. We used interannual trends in the Normalized Difference Vegetation Index (NDVI) time series data derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) to assess vegetation response, which we define as the average potential photosynthetic activity through the summer monsoon. Topography, fire severity and restoration treatment were obtained and used to explain post-fire vegetation response. We applied parametric (Multiple Linear Regressions-MLR) and non-parametric tests (Classification and Regression Trees-CART) to analyze effects of fire severity, terrain and pre-fire restoration treatments (variable used in CART) on post-fire vegetation response. MLR results showed strong relationships between vegetation response and environmental factors (p < 0.1), however the explanatory factors changed among treatments. CART results showed that beside fire severity and topography, pre-fire treatments strongly impact post-fire vegetation response. Results for these three fires show that pre-fire restoration conditions along with local environmental factors constitute key processes that modify post-fire vegetation response. View Full-TextKeywords:
wildfire; remote sensing; restoration; disturbance; experiment; statistical models
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).
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